# Lampedusa 1
rdlamp1 <- RDestimate(factor ~ lamp, data = data61, cutpoint = 0, bw = 10)
summary(rdlamp1) 
rdlamp2 <- RDestimate(factor ~ lamp, data = data62, cutpoint = 0, bw = 10)
summary(rdlamp2)

# 31.08.2014
rd1 <- RDestimate(factor ~ one | cntry, data = data71, cutpoint = 0, bw = 10)
summary(rd1)
rd1b <- RDestimate(factor ~ one | cntry, data = data72, cutpoint = 0, bw = 10)
summary(rd1b)

# 14.09.2014
rd2 <- RDestimate(factor ~ two | cntry, data = data71, cutpoint = 0, bw = 10)
summary(rd2)
rd2b <- RDestimate(factor ~ two | cntry, data = data72, cutpoint = 0, bw = 10)
summary(rd2b)

# 08.02.2015
rd3 <- RDestimate(factor ~ three | cntry, data = data71, cutpoint = 0, bw = 10)
summary(rd3)
rd3b <- RDestimate(factor ~ three | cntry, data = data72, cutpoint = 0, bw = 10)
summary(rd3b)

# 18.04.2015
rd4 <- RDestimate(factor ~ four | cntry, data = data71, cutpoint = 0, bw = 10)
summary(rd4)
rd4b <- RDestimate(factor ~ four | cntry, data = data72, cutpoint = 0, bw = 10)
summary(rd4b)

# 21.09.2016
rd5 <- RDestimate(factor ~ five | cntry, data = data81, cutpoint = 0, bw = 10)
summary(rd5)
rd5b <- RDestimate(factor ~ five | cntry, data = data82, cutpoint = 0, bw = 10)
summary(rd5b)

# 02.11.2016
rd6 <- RDestimate(factor ~ six | cntry, data = data81, cutpoint = 0, bw = 10)
summary(rd6)
rd6b <- RDestimate(factor ~ six | cntry, data = data82, cutpoint = 0, bw = 10)
summary(rd6b)

# 14.11.2016
rd7 <- RDestimate(factor ~ seven | cntry, data = data81, cutpoint = 0, bw = 10)
summary(rd7)
rd7b <- RDestimate(factor ~ seven | cntry, data = data82, cutpoint = 0, bw = 10)
summary(rd7b)

# 14.01.2017
rd8 <- RDestimate(factor ~ eight | cntry, data = data81, cutpoint = 0, bw = 10)
summary(rd8)
rd8b <- RDestimate(factor ~ eight | cntry, data = data82, cutpoint = 0, bw = 10)
summary(rd8b)

# 20.02.2017
rd9 <- RDestimate(factor ~ nine | cntry, data = data81, cutpoint = 0, bw = 10)
summary(rd9)
rd9b <- RDestimate(factor ~ nine | cntry, data = data82, cutpoint = 0, bw = 10)
summary(rd9b)

# 23.03.2017
rd10 <- RDestimate(factor ~ ten | cntry, data = data81, cutpoint = 0, bw = 10)
summary(rd10)
rd10b <- RDestimate(factor ~ ten | cntry, data = data82, cutpoint = 0, bw = 10)
summary(rd10b)

# 16.04.2017
rd11 <- RDestimate(factor ~ eleven | cntry, data = data81, cutpoint = 0, bw = 10)
summary(rd11)
rd11b <- RDestimate(factor ~ eleven | cntry, data = data82, cutpoint = 0, bw = 10)
summary(rd11b)

# 17.06.2017
rd12 <- RDestimate(factor ~ twelve | cntry, data = data81, cutpoint = 0, bw = 10)
summary(rd12)
rd12b <- RDestimate(factor ~ twelve | cntry, data = data82, cutpoint = 0, bw = 10)
summary(rd12b)

# 18.01.2019
rd13 <- RDestimate(factor ~ thirteen | cntry, data = data91, cutpoint = 0, bw = 10)
summary(rd13)
rd13b <- RDestimate(factor ~ thirteen | cntry, data = data92, cutpoint = 0, bw = 10)
summary(rd13b)

### 18.01.2019 ITALY ONLY ###
rd13IT <- RDestimate(factor ~ thirteen, data = data91IT, cutpoint = 0, bw = 10)
summary(rd13IT)
rd13bIT <- RDestimate(factor ~ thirteen, data = data92IT, cutpoint = 0, bw = 10)
summary(rd13bIT)

# 04.12.2019
rd14 <- RDestimate(factor ~ fourteen | cntry, data = data91, cutpoint = 0, bw = 10)
summary(rd14)
rd14b <- RDestimate(factor ~ fourteen | cntry, data = data92, cutpoint = 0, bw = 10)
summary(rd14b)

# 17.12.2021
rd15 <- RDestimate(factor ~ fifteen | cntry, data = data101, cutpoint = 0, bw = 10)
summary(rd15)
rd15b <- RDestimate(factor ~ fifteen | cntry, data = data102, cutpoint = 0, bw = 10)
summary(rd15b)

### 17.12.2021 ITALY ONLY ###
rd15IT <- RDestimate(factor ~ fifteen, data = data101IT, cutpoint = 0, bw = 10)
summary(rd15IT)
rd15bIT <- RDestimate(factor ~ fifteen, data = data102IT, cutpoint = 0, bw = 10)
summary(rd15bIT)

# META #
ITntmeta <- RDestimate(factor ~ run, data = ITnt, cutpoint = 0, bw = 10)
summary(ITntmeta)
ITonmeta <- RDestimate(factor ~ run, data = ITon, cutpoint = 0, bw = 10)
summary(ITonmeta)

CCntmeta <- RDestimate(factor ~ run | cntry, data = CCnt, cutpoint = 0, bw = 10)
summary(CCntmeta)
CConmeta <- RDestimate(factor ~ run | cntry, data = CCon, cutpoint = 0, bw = 10)
summary(CConmeta)